Radar Wind Profilers: A Widespread but Unused Remote Sensing Tool for Migration Ornithologists

Post provided by Nadja Weisshaupt

Snapshot of nocturnal waterfowl migration in front of the lunar disk. ©N. Weisshaupt

Snapshot of nocturnal waterfowl migration in front of the lunar disk. ©N. Weisshaupt

Each year an uncountable number of airborne organisms, mainly birds and insects, venture out on long journeys across the globe. In particular, the mass movements of birds have fascinated humankind for hundreds of years and inspired a wealth of increasingly sophisticated studies. The development and improvement of individual tracking devices in animal research and has provided amazing insights into such extensive journeys. Study of mass movements of biological organisms is still a challenge on continent-wide or cross-continental scales.

One tool that can effectively track and/or monitor large numbers of birds is radar technology. Radars offer many advantages over other methods such as visual counts or ringing. They’re less expensive, need less effort, offer better visibility and detectability, and are more applicable for large-scale monitoring. Networks of meteorological radars (as opposed to individual radars) seem particularly promising for large-scale studies. Continue reading


Meta-Analysis: How to Increase the Reach of Your Research and Make it Longer Lasting

Post provided by Katharina Gerstner

Like each coral, every single primary research study contributes to the larger picture.  © Wise Hok Wai Lum

Like each coral, every single primary research study contributes to the larger picture. © Wise Hok Wai Lum

Quantitative syntheses of primary research studies (meta-analysis) are being used more and more in ecological and evolutionary research. So knowing the basics of how meta-analysis works is important for every researcher. Meta-analytical thinking also encourages us scientists to see each single primary research study as a substantial contribution to a larger picture.

To be included in a meta-analysis, relevant primary research studies must be easy to find and basic information about the methods and results must be thoroughly, clearly and transparently reported. Moreover, papers with accessible data are the most useful for meta-analyses. Many published papers provide this information, but it’s not unusual for essential data to be omitted. Studies that are missing these details can’t be used in meta-analyses, which limits their reach. Continue reading

Who to Trust? The IDEA Protocol for Structured Expert Elicitation

Post provided by Victoria Hemming and Mark Burgman

Expert judgement is used to predict current and future trends for Koala populations across Australia

Expert judgement is used to predict current and future trends for Koala populations across Australia

New technologies provide ecologists with unprecedented means for informing predictions and decisions under uncertainty. From drones and apps that capture data faster and cheaper than ever before, to new methods for modelling, mapping and sharing data.

But what do you do when you don’t have data (or the data you have is incomplete or uninformative), but decisions need to be made?

In ecology, decisions often need to be made with imperfect or incomplete data. In these circumstances, expert judgement is relied upon routinely. Some examples include threatened species listing decisions, weighing up the cost and benefit of management actions, and environmental impact assessments.

We use experts to answer questions such as:

These are questions about facts in the form of quantities and probabilities for which we simply can’t collect the data. Continue reading

Applications of Multi-Criteria Decision Analysis in Conservation Research

Post provided by Blal Adem Esmail & Davide Geneletti

Comparing Apples and Oranges

©Ruth Hartnup

In real-life situations, it is far more common for decisions to be based on a comparison between things that can’t be judged on the same standards. Whether you’re choosing a dish or a house or an area to prioritise for conservation you need to weigh up completely different things like cost, size, feasibility, acceptability, and desirability.

Those three examples of decisions differ in terms of complexity – you’d need specific expert knowledge and/or the involvement of other key stakeholders to choose conservation prioritisation areas, but probably not to pick a dish. The bottom line is they all require evaluating different alternatives to achieve the desired goal. This is the essence of multi-criteria decision analysis (MCDA). In MCDA the pros and cons of different alternatives are assessed against a number of diverse, yet clearly defined, criteria. Interestingly, the criteria can be expressed in different units, including monetary, biophysical, or simply qualitative terms. Continue reading

Using Focus Group Discussions in Conservation Research

Post provided by Christina Derrick

Focus Group Discussions: What are They and Why Use Them?

A focus group discussion with local farmers in Trans Mara district, Kenya, carried out by Tobias O. Nyumba (co-author)

A focus group discussion with local farmers in Trans Mara district, Kenya, carried out by Tobias O. Nyumba (co-author)

To paraphrase Nelson Mandela: ultimately, conservation is about groups of people. On a global scale it’s our collective human footprint that drives habitat destruction and species extinction, and the joint action of large groups that makes positive change. At a smaller scale, groups of people make decisions about conservation policy or management. In turn, communities of people feel the positive or negative effects of these actions, directly or indirectly. From global to local scales, groups of people make changes and groups of people feel the effects of those changes.

To improve conservation action and understand how decisions affect communities on the ground we need to talk to those communities. This is where focus group discussions become an asset to conservation research. They bring participants together in the same place where they can draw from their own personal beliefs and experiences, and those of other group members in a collective discussion. The researcher takes more of a backseat (facilitator) role in focus group discussions compared to interviews, allowing the group conversation to evolve organically. We can get a more holistic view of a situation from this method than from one-on-one interviews alone. Also, as respondents are interviewed at the same time and in the same place, travelling times and costs can be reduced for the researcher. Continue reading

Code-Based Methods and the Problem of Accessibility

Post provided by Jamie M. Kass, Matthew E. Aiello-Lammens, Bruno Vilela, Robert Muscarella, Cory Merow and Robert P. Anderson

The namesake of our software and founder of the field of biogeography, Alfred Russel Wallace. Photo ©G. W. Beccaloni

The namesake of our software and founder of the field of biogeography, Alfred Russel Wallace. Photo ©G. W. Beccaloni

In ecology, new methods are increasingly being accompanied by code, and sometimes even full command-line software packages (usually in R). This is great, as it makes analyses more reproducible and transparent, which is essential for the development of open science. In an ideal world, code would have informative annotation, generalized functions for multipurpose use, and be written in a legible and consistent manner. After all, the code may be used by ecologists with a wide range of programming experience.

In reality, code is often poorly commented (or not commented at all!), hard to reuse for other projects, and difficult to interpret. To add to that, most code isn’t actively maintained, so users are on their own if they try to commandeer it for new purposes. Further, ecologists with little or no programming knowledge are unlikely to benefit from methods that exist only as poorly documented code. In a positive development, some new methods are accessible through software with graphic user interfaces (GUIs) developed by programmers spending significant time and effort. But too often these end up as tools with flashy controls and insufficient instruction manuals. Continue reading

Editor Recommendation: A Practical Guide to Structured Expert Elicitation Using the IDEA Protocol

Post provided by Barbara Anderson

Today is International Women’s Day to mark the occasion I have the privilege of recommending, ‘A practical guide to structured expert elicitation using the IDEA protocol by Victoria Hemming et al. The IDEA behind the IDEA protocol – ‘Investigate’, ‘Discuss’, ‘Estimate’ and ‘Aggregate’ – is to provide a framework for Structured Expert Elicitation.

As a quantitative ecologist, I sometimes attempt to model species’ abundance and distribution changes in response to environmental change. Often these are species that, for one reason or another, we know a lot about. They may be high profile species of conservation concern, or have some economic or cultural importance. Some are simply model species that many people have studied because they’re easy to study because many people have studied them. Just as often though, we’re missing crucial data on one or more parameters. Frustratingly we don’t always have the time or resources to collect the new ecological or biological data required. Continue reading

Editor Recommendation: A Multi-State Species Distribution Modelling Framework for Species Using Distinct Habitats

Post provided by Jana McPherson

© Amélie Augé

© Amélie Augé

Correlative distribution models have become essential tools in conservation, macroecology and ecology more generally. They help turn limited occurrence records into predictive maps that help us get a better sense of where species might be found, which areas might be critical for their protection, how large their range currently is, and how it might change with climate change, urban encroachment or other forms of habitat conversion.

It can be frustrating, however, when species distribution models (and the predictive maps they produce) don’t adequately capture what we already know about the habitat needs of a species. A major challenge to date has been to represent the environmental needs of species that require distinct habitats during different life stages or behavioural states. Rainbow parrotfish (Scarus guacamaia), for example, spend their youth sheltered from predators in mangrove areas before moving onto coral reefs, and European nightjars (Caprimulgus europaeus) breed in heathland but require access to grazed grassland for foraging. Correlative distribution models confronted with occurrence records from both life stages or behavioural modes tend to produce poor predictive maps because they confound these distinct requirements. Continue reading

Editor Recommendation: How Do Trait Distributions Differ Across Species and Their Environments?

Post provided by Pedro Peres-Neto

The rise of trait ecology led to many quantitative frameworks to understand the underlying rules that determine how species are assembled into local communities from regional pools. Ecologists are interested in understanding whether environmental features select for particular traits that optimise local fitness and regulate species co-existence.

In ‘Assessing the joint behaviour of species traits as filtered by environment’, Erin Schliep and her co-authors aimed to develop a joint probabilistic model under a Bayesian framework to help explain the correlations among traits and how trait distributions differ across species and their environments. The end product is a model of trait-environmental relationships that takes full advantage of information on intra- and interspecific variation typically found within and among species.  Continue reading

Editor recommendation: Predicting Animal Behaviour Using Deep Learning

Post provided by Jana McPherson

Common guillemots were one of the species used in this study. ©Richard Crossley

Common guillemots were one of the species used in this study. ©Richard Crossley

Understanding key habitat requirements is critical to the conservation of species at risk. For highly mobile species, discerning what is key habitat as opposed to areas that are simply being traversed (perhaps in the search for key habitats) can be challenging. For seabirds, in particular, it can be difficult to know which areas in the sea represent key foraging grounds. Devices that record birds’ diving behaviour can help shed light on this, but they’re expensive to deploy. In contrast, devices that record the birds’ geographic position are more commonly available and have been around for some time.

In their recent study entitled ‘Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds,’ Ella Browning and her colleagues made use of a rich dataset on 399 individual birds from three species, some equipped with both global positioning (GPS) and depth recorder devices, others with GPS only. The data allowed them to test whether deep learning methods can identify when the birds are diving (foraging) based on GPS data alone. Results were highly promising, with top models able to distinguish non-diving and diving behaviours with 94% and 80% accuracy. Continue reading